Object detection has a wide range of applications. For example, face detection may be used in human-computer interaction, photo-album management, biometric authentication, video surveillance, automatic-focus imaging, and a variety of other vision systems. Human detection may be used in video surveillance, advanced driver assistance systems, and the like. Other object detection examples include traffic monitoring, automated vehicle parking, character recognition, manufacturing quality control, object counting and quality monitoring.
In some existing object detection systems, a Viola-Jones cascade detection framework is used. In the Viola-Jones cascade detection framework, an input image is scanned with a sliding window to probe whether or not a target exists in the window using a cascade classifier. Such methods ma employ feature-based classifiers, which are complicated to implement. Additionally, such methods are computationally intensive. Various software and hardware implementations have been proposed, however the proposed implementations have limitations, particularly as image and video resolutions increase.
Since object detection may be used in such a side variety of applications, it may be desirable to make object detection execute efficiently.